Comparing machine learning models in R

#-----------------------------------------
# SET UP THE PARAMETER SPACE SEARCH GRID
ctrl <- trainControl(method="repeatedcv",	        # use repeated 10fold cross validation
		     repeats=5,	                        # do 5 repititions of 10-fold cv
		     summaryFunction=twoClassSummary,	# Use AUC to pick the best model
		     classProbs=TRUE)
# Note that the default search grid selects 3 values of each tuning parameter
#
grid <- expand.grid(.interaction.depth = seq(1,7,by=2), # look at tree depths from 1 to 7
		    .n.trees=seq(10,100,by=5),	        # let iterations go from 10 to 100
		    .shrinkage=c(0.01,0.1))		# Try 2 values of the learning rate parameter
 
# BOOSTED TREE MODEL										
set.seed(1)
names(trainData)
trainX <-trainData[,4:61]
 
registerDoParallel(4)		                        # Registrer a parallel backend for train
getDoParWorkers()
 
system.time(gbm.tune <- train(x=trainX,y=trainData$Class,
				method = "gbm",
				metric = "ROC",
				trControl = ctrl,
				tuneGrid=grid,
				verbose=FALSE))
 
#---------------------------------
# SUPPORT VECTOR MACHINE MODEL
#
set.seed(1)
registerDoParallel(4,cores=4)
getDoParWorkers()
system.time(
	svm.tune <- train(x=trainX, y= trainData$Class,
				method = "svmRadial",
				tuneLength = 9,		# 9 values of the cost function
				preProc = c("center","scale"),
				metric="ROC",
				trControl=ctrl)	        # same as for gbm above
)	
#-----------------------------------
# COMPARE MODELS USING RESAPMLING
# Having set the seed to 1 before running gbm.tune and svm.tune we have generated paired samplesfor comparing models using resampling.
#
# The resamples function in caret collates the resampling results from the two models
rValues <- resamples(list(svm=svm.tune,gbm=gbm.tune))
rValues$values
#---------------------------------------------
# BOXPLOTS COMPARING RESULTS
bwplot(rValues,metric="ROC")		# boxplot